diff --git a/1_to_1_multi_layer.ipynb b/1_to_1_multi_layer.ipynb
index 839daa0..ce6cd94 100644
--- a/1_to_1_multi_layer.ipynb
+++ b/1_to_1_multi_layer.ipynb
@@ -208,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -223,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -259,7 +259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -343,7 +343,7 @@
     "        \n",
     "        \n",
     "        \n",
-    "    def fit(self, minibatches, epochs, print_step, checkpoint = 10, patience = 80):\n",
+    "    def fit(self, minibatches, epochs, print_step, checkpoint = 10, patience = 15):\n",
     "        self.loss_list = []\n",
     "        patience_cnt = 0\n",
     "        epoche_save = 0\n",
@@ -353,8 +353,9 @@
     "        for iep in range(epochs):\n",
     "            loss = 0\n",
     "            \n",
-    "            #Here I iterate through the batches\n",
-    "            for batch in range(len(minibatches)):\n",
+    "            batches = len(minibatches)\n",
+    "            #Here I iterate over the batches\n",
+    "            for batch in range(batches):\n",
     "            #### Here I train the RNNcell\n",
     "            #### The X is the time series, the Y is shifted by 1 time step\n",
     "                train, target = minibatches[batch]\n",
@@ -363,6 +364,8 @@
     "            \n",
     "                loss += self.sess.run(self.cost, feed_dict={self.X:train, self.Y:target})\n",
     "            \n",
+    "            #Normalize loss over number of batches\n",
+    "            loss /= batches\n",
     "            self.loss_list.append(loss)\n",
     "            \n",
     "            #print(loss)\n",
@@ -374,17 +377,13 @@
     "                epoche_save = iep\n",
     "            \n",
     "            #early stopping with patience\n",
-    "            if iep > 1 and abs(self.loss_list[iep]-self.loss_list[iep-1]) < 0.05:\n",
+    "            if iep > 1 and abs(self.loss_list[iep]-self.loss_list[iep-1]) < 0.005:\n",
     "                patience_cnt += 1\n",
     "                #print(\"Patience now at: \", patience_cnt, \" of \", patience)\n",
+    "                \n",
     "                if patience_cnt + 1 > patience:\n",
     "                    print(\"Early stopping at epoch \", iep, \", difference: \", abs(self.loss_list[iep]-self.loss_list[iep-1]))\n",
-    "                    \n",
-    "                    #Set model back to the last checkpoint if performance was better\n",
-    "                    if self.loss_list[epoche_save] < self.loss_list[iep]:\n",
-    "                        self.load(folder)\n",
-    "                        print(\"Last checkpoint at epoch \", epoche_save, \" loaded\")\n",
-    "                        print(\"Performance at last checkpoint is \" ,self.loss_list[iep] - self.loss_list[epoche_save], \" better\" )\n",
+    "                    print(\"Cost: \",loss)\n",
     "                    break\n",
     "            \n",
     "            if iep%print_step==0:\n",
@@ -392,6 +391,12 @@
     "                print(\"Cost: \",loss)\n",
     "                print(\"Patience: \",patience_cnt, \"/\", patience)\n",
     "                print(\"Last checkpoint at: Epoch \", epoche_save, \"\\n\")\n",
+    "        \n",
+    "        #Set model back to the last checkpoint if performance was better\n",
+    "        if self.loss_list[epoche_save] < self.loss_list[iep]:\n",
+    "            self.load(folder)\n",
+    "            print(\"Last checkpoint at epoch \", epoche_save, \" loaded\")\n",
+    "            print(\"Performance at last checkpoint is \" ,self.loss_list[iep] - self.loss_list[epoche_save], \" better\" )\n",
     "            \n",
     "            \n",
     "        \n",
@@ -410,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -427,9 +432,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From c:\\users\\sa_li\\anaconda3\\envs\\rnn-tf-ker\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\datasets\\base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use the retry module or similar alternatives.\n"
+     ]
+    }
+   ],
    "source": [
     "tf.reset_default_graph()\n",
     "rnn = RNNPlacePrediction(time_steps=timesteps, future_steps=future_steps, ninputs=ninputs, \n",
@@ -438,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -447,7 +462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {
     "scrolled": true
    },
@@ -457,15 +472,20 @@
      "output_type": "stream",
      "text": [
       "Epoch number  0\n",
-      "Cost:  65161.071533203125\n",
-      "Patience:  0 / 80\n",
+      "Cost:  3.893127314587857\n",
+      "Patience:  0 / 5\n",
       "Last checkpoint at: Epoch  0 \n",
-      "\n"
+      "\n",
+      "Early stopping at epoch  10 , difference:  0.002693013941988287\n",
+      "Cost:  3.91306800537921\n",
+      "INFO:tensorflow:Restoring parameters from ./rnn_model_lstm[50,40,30,20,10]c_checkpoint/rnn_basic\n",
+      "Last checkpoint at epoch  0  loaded\n",
+      "Performance at last checkpoint is  0.019940690791353077  better\n"
      ]
     }
    ],
    "source": [
-    "rnn.fit(minibatches, epochs=5000, print_step=500)"
+    "rnn.fit(minibatches, epochs=22, print_step=10)"
    ]
   },
   {
@@ -474,6 +494,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "plt.plot(rnn.loss_list)\n",
+    "plt.xlabel(\"Epoch\")\n",
+    "plt.ylabel(\"Cost\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "#save in a folder that describes the model\n",
     "folder = \"./rnn_model_\" + str(rnn._) + \"_\"  + str(len(rnn.ncells)) + \"l_\" + str(rnn.ncells).replace(\" \",\"\") + \"c/rnn_basic\"\n",
     "rnn.save(folder)"
@@ -481,16 +513,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
-    "###rnn.load(folder)###"
+    "#folder = \"./trained_models/rnn_model_\" + str(rnn._) + \"_\"  + str(len(rnn.ncells)) + \"l_\" + str(rnn.ncells).replace(\" \",\"\") + \"c/rnn_basic\"\n",
+    "#rnn.load(folder)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -499,18 +532,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
     "#Here I predict based on my test set\n",
     "\n",
-    "#test_pred = rnn.predict(test_input)"
+    "test_pred = rnn.predict(test_input)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -521,13 +554,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.3867254"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#Here I evaluate my model on the test set based on mean_squared_error\n",
     "\n",
-    "#rnn.sess.run(rnn.cost, feed_dict={rnn.X:test_input, rnn.Y:test_target})"
+    "rnn.sess.run(rnn.cost, feed_dict={rnn.X:test_input, rnn.Y:test_target})"
    ]
   },
   {
@@ -535,9 +579,7 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "print(1%10)"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -546,15 +588,7 @@
     "scrolled": true
    },
    "outputs": [],
-   "source": [
-    "z = 0\n",
-    "for _ in range(len(rnn.loss_list)):\n",
-    "    if abs(rnn.loss_list[_]-rnn.loss_list[_-1]) < 0.5:\n",
-    "        z += 1\n",
-    "        print(_)\n",
-    "\n",
-    "print(z)"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
diff --git "a/trained_models/rnn_model_lstm_5l_\13350,40,30,20,10\135c/checkpoint" "b/trained_models/rnn_model_lstm_5l_\13350,40,30,20,10\135c/checkpoint"
new file mode 100644
index 0000000..774833f
--- /dev/null
+++ "b/trained_models/rnn_model_lstm_5l_\13350,40,30,20,10\135c/checkpoint"
@@ -0,0 +1,3 @@
+model_checkpoint_path: "rnn_basic"
+all_model_checkpoint_paths: "..\\rnn_model_lstm[50,40,30,20,10]c_checkpoint\\rnn_basic"
+all_model_checkpoint_paths: "rnn_basic"
diff --git "a/trained_models/rnn_model_lstm_5l_\13350,40,30,20,10\135c/rnn_basic.data-00000-of-00001" "b/trained_models/rnn_model_lstm_5l_\13350,40,30,20,10\135c/rnn_basic.data-00000-of-00001"
new file mode 100644
index 0000000..d91c1ce
--- /dev/null
+++ "b/trained_models/rnn_model_lstm_5l_\13350,40,30,20,10\135c/rnn_basic.data-00000-of-00001"
Binary files differ
diff --git "a/trained_models/rnn_model_lstm_5l_\13350,40,30,20,10\135c/rnn_basic.index" "b/trained_models/rnn_model_lstm_5l_\13350,40,30,20,10\135c/rnn_basic.index"
new file mode 100644
index 0000000..361718e
--- /dev/null
+++ "b/trained_models/rnn_model_lstm_5l_\13350,40,30,20,10\135c/rnn_basic.index"
Binary files differ
diff --git "a/trained_models/rnn_model_lstm_5l_\13350,40,30,20,10\135c/rnn_basic.meta" "b/trained_models/rnn_model_lstm_5l_\13350,40,30,20,10\135c/rnn_basic.meta"
new file mode 100644
index 0000000..b941ef6
--- /dev/null
+++ "b/trained_models/rnn_model_lstm_5l_\13350,40,30,20,10\135c/rnn_basic.meta"
Binary files differ